Abstract
Understanding the language and things recognition of children with ASD is crucial for assessing the appropriateness of support for them. However, it is difficult for others to comprehend these aspects. Previous research has attempted to replicate ASD-related visual characteristics to promote understanding, but such approaches have been insufficient in capturing the highly individualistic traits of ASD. To address this, our study aims to contribute to the elucidation of language cognition mechanisms in children with ASD and the development of new support methods. Specifically, we focus on (1) generating diverse personas of children with ASD using LLMs and (2) establishing ASDKidsPersonaLLM, which incorporates these personas. In this paper, we investigate prompts that enable the LLM to distinguish between stories created by children with ASD and those by typically developing children. We investigated whether the LLM identifies stories created by children with ASD by constructing a five-choice QA dataset. We improved classification accuracy to 33% by incorporating inferred problem-solving processes for examples into the prompt.
